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Feature papers represent the most advanced research with significant potential for high impact in the field. A FeaturePaper should be a substantial original Article that involves several techniques or approaches, provides an outlook forfuture research directions and describes possible research applications.
Abstract:Unmanned aerial vehicle (UAV) systems are heavily adopted nowadays to collect high-resolution imagery with the purpose of documenting and mapping environment and cultural heritage. Such data are currently processed by programs based on the Structure from Motion (SfM) concept, coming from the computer vision community, rather than from classical photogrammetry. It is interesting to check whether some widely accepted rules coming from old-fashioned photogrammetry still holds: the relation between accuracy and ground sampling distance (GSD), the ratio between the vertical and horizontal accuracy, accuracy estimated on ground control points (GCPs) vs. that estimated with check points (CPs) also in relation to their ratio and distribution. To face the envisaged aspects, the paper adopts a comparative approach, as several programs are used and numerous configurations considered. The paper illustrates the dataset adopted, the carefully tuned processing strategies and bundle block adjustment (BBA) results in terms of accuracy for both GCPs and CPs. Finally, a leave-one-out (LOO) cross-validation strategy is proposed to assess the accuracy for one of the proposed configurations. Some of the reported results were previously presented in the 5th GISTAM Conference.Keywords: UAV; bundle block adjustment; accuracy evaluation; proprietary software; open source software; cross-validation
As with Visual SfM, a number of photographs must be taken completing a 360 degrees of the object to be scanned. In this instance, another Ogham Stone from Burnham House. Forty four images in total. This is not a high number for 3D data map, but at this point, I was more interested in learning the process. For more accurate results, more photographs would have to be taken.
In the Agisoft Photoscan software I worked through the workflow system. Workflow is the series of activities that are necessary to complete a task; each step in a workflow has a specific step before it and a specific step after it, with the exception of the first step [Rouse 2016]. The workflow can be different, depending on the final model you want, and how you are going to use the model. For research purposes, you may only need a mesh, but for visuals and animation, you may need texture. The speed at which the software will process the data set, and the resultant 3D model, will depend on how you want to use the data after you have created the model. This is important as you can set various levels such as accuracy and quality. This will be discussed in more detail as I go though the steps to creating a 3D model of an Ogham Stone.
The roughness property of rocks is significant in engineering studies due to their mechanical and hydraulic performance and the possibility of quantifying flow velocity and predicting the performance of wells and rock mass structures. However, the study of roughness in rocks is usually carried out through 2D linear measurements (through mechanical profilometer equipment), obtaining a coefficient that may not represent the entire rock surface. Thus, based on the hypothesis that it is possible to quantify the roughness coefficient in rock plugs reconstructed three-dimensionally by the computer vision technique, this research aims to an alternative method to determine the roughness coefficient in rock plugs. The point cloud generated from the 3D model of the photogrammetry process was used to measure the distance between each point and a calculated fit plane over the entire rock surface. The roughness was quantified using roughness parameters (\(R_a\)) calculated in hierarchically organized regions. In this hierarchical division, the greater the quantity of division analyzed, the greater the detail of the roughness. The main results show that obtaining the roughness coefficient over the entire surface of the three-dimensional model has peculiarities that would not be observed in the two-dimensional reading. From the 2D measurements, mean roughness values (\(R_a\)) of \(0.35\,\upmu \hbox {m}\) and \(0.235\,\upmu \hbox {m}\) were obtained for samples 1 and 2, respectively. By the same method, the results of the \(R_a\) coefficient applied three-dimensionally over the entire rocky surface were at most \(0.165\,\upmu \hbox {m}\) and \(0.166\,\upmu \hbox {m}\), respectively, showing the difference in values along the surface and the importance of this approach.
The roughness study is applied in the risk assessment for possible water percolation in tunnels and natural coal mines, identification, and exploration of geothermal reservoirs, the capture of \(CO_2\), studies for disposal of underground deposits of radioactive material, capture and use of groundwater, and classification and study of natural well fractures as a means of disposal and storage of crude oil7,11,12,13. To determine the roughness in rock masses joints, research was carried out to establish a relationship between the joint opening distribution and the fluid conductivity, and a greater roughness on any surface hinders the flow and facilitates the union between separate layers9,14.
These techniques have advantages over conventional acquisition methods of physical samples of rock, such as the obtainment in inaccessible places, less dependence on the professional working in the field experience, the possibility of a greater quantity of rock samples with less manual work, and the possibility of 3D representation of the entire rock sample16,18. Another incentive for the increment of 3D models is the improvement of computational methods. Currently, two-dimensional images and referenced points obtained by photogrammetry or by laser scanning are efficiently processed by algorithms, reducing manual work and generating more reliable results17.
Some strategies to measure the roughness coefficient in rock mass joints using 3D models were developed to support the traditional techniques. Therefore, it is natural to carry out research that applies functional methods but is used in other areas of study to obtain roughness coefficients in digital representations of rocks16,17,19. Tonietto et al.20,21 calculates the roughness of a block substrate (used to build masonry walls) in height and area measurements to favor adhesion by contact area. The authors used a point cloud obtained by laser scanning (LiDAR), where each point contains position and elevation information. The roughness of the block was measured between the height difference of each point and a created plane that best represented the group of points (fit plane), forming peak areas (group of points above the fit plane) and valley areas (group of points below the fit plane)21.
Through the related works, it is possible to find limitations and research gaps to be filled. Some methods used two-dimensional linear results, which do not represent the entire rock surface17. The use of laser scanners in their methodology is another limitation found. This equipment has a high cost of acquisition and operation, and is laborious, as the rock samples require greater preparation than photogrammetry. With photogrammetry, it is possible to acquire rock samples by the non-destructive method in the field. In addition, it generates higher productivity and lower cost compared to laser scanners22.
The 3D roughness measurements on rock surfaces are essential for obtaining this coefficient to be realistic on the entire rock surface. In this sense, the method for results analysis in hierarchical division allows obtaining the average roughness over the total surface of rocks or rocky outcrops20. The main advance of this method in relation to the studies shown above is the ease of increasing (or decreasing) the level of details of results according to the interest of the work performed, changing the analysis division. Furthermore, it can be applied both at the laboratory rock sample level and for application on large rocky outcrops.
This work aims at an alternative method to obtain the roughness coefficient in rock plugs samples. This objective seeks to confirm the hypothesis that it is possible to quantify the roughness coefficient in 3D reconstructed rock plugs using the computer vision technique. However, it was necessary to make adaptations to the method20. The achievement of results was in 2D linear measures (in addition to the three-dimensional), so it can be directly validated and compared. Another adaptation performed to the method to geomechanics was obtaining the roughness parameters for the global plane of the rock sample and the representation of the roughness data in a localized way (node grid of the quadtree)20.
Thus, the roughness parameters from the plane coefficients were calculated, defining the mean roughness (\(R_a\)). A local analysis of the results is essential for determining a roughness signature composed of the values of each region subdivided in a hierarchical way. In this division, the values found in the last level are the minimum area values defined by the user for roughness evaluation. With this roughness signature (\(R_a\)), the authors compared each surface in a standard way. After roughness identification, the results are processed to generate graphs and other parameter information. In this stage, the mean roughness coefficients (\(R_a\)) are obtained20.
The rock samples used in the research are from the Bangú quarry in the municipality of Rio de Janeiro, Brazil (Fig. 3). This place is predominantly composed of gneiss rocks. The roughness analysis in them is capital for the identification, classification, and mechanisms of fracture formation. Gneiss rocks are metamorphic rocks. They originated from pre-existing rocks through chemical, mineralogical, textural, structural changes, or a combination of these factors, and this transformation occurs through changes in temperature and pressure23. Thus, the roughness and fluid flow characteristics in metamorphic rocks are related to the rocks that form them, requiring a particular study of each rock sample to understand their behavior24. 781b155fdc